Counts read classes found in a read_classes data frame produced by the ninetails pipeline.

Tabulates the prediction information contained in the read_classes data frame. Counts can be computed at two levels of granularity (detailed or crude) and optionally stratified by a grouping variable.

Usage

count_class(class_data, grouping_factor = NA, detailed = TRUE)

Arguments

class_data

Data frame or tibble containing read_classes predictions produced by the ninetails pipeline.

grouping_factor

Character string (default NA). Name of a column in class_data to use as a grouping variable (e.g. "sample_name").

detailed

Logical [TRUE]. If TRUE, counts are provided based on the comments column (fine-grained). If FALSE, counts are provided based on the class column (crude: decorated / blank / unclassified only).

Value

A tibble with columns for the grouping variable (if provided), the classification label (comments or class), and n (the count).

Details

When detailed = TRUE, counts are based on the comments column, which carries fine-grained labels such as "YAY" (tail with non-A residues detected). When detailed = FALSE, counts are based on the class column with three broad categories: "decorated", "blank", and "unclassified".

See also

read_class_single and read_class_multiple for loading class data, count_residues for the analogous residue-level counts, merge_nonA_tables for combining class and residue data.

Examples

if (FALSE) { # \dontrun{

class_counted <- ninetails::count_class(
  class_data = out[[1]],
  grouping_factor = NA,
  detailed = TRUE)

} # }